PSTAT 231: STAT MACHINE LEARN
University of California, Santa Barbara
Statistical Machine Learning is used to discover patterns and relationships in large data sets. Topics will include: data exploration, classification and regression trees, random forests, clustering and association rules. Bui lding predictive models focusing on model selection, model comparison and p erformance evaluation. Emphasis will be on concepts, methods and data analy sis; and students are expected to complete a significant class project, ind ividual or team based, using real world data.
Average GPA: 3.69
Grade distribution records: 604 students across 30 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 52 | 8.6% |
| A | 325 | 53.8% |
| A- | 88 | 14.6% |
| B+ | 63 | 10.4% |
| B | 43 | 7.1% |
| B- | 13 | 2.2% |
| C+ | 2 | 0.3% |
| C | 3 | 0.5% |
| C- | 3 | 0.5% |
| D | 3 | 0.5% |
| F | 8 | 1.3% |
| S | 1 | 0.2% |
Based on 604 student grade records across 30 terms and 10 professors.
Instructors
- Coburn K M 176 students, Average GPA 3.78
- Oh Sang-Yun 103 students, Average GPA 3.52
- Franks A 93 students, Average GPA 3.60
- Feldman R 69 students, Average GPA 3.83
- Yu G 52 students, Average GPA 3.55
- Li Zhijian 41 students, Average GPA 3.74
- Ruiz T D 25 students, Average GPA 3.74
- Taufer E 18 students, Average GPA 3.70
- Gopalan G 14 students, Average GPA 3.87
- Kloke J 13 students, Average GPA 3.82